Interpretive Summary:
It has been established that there is a strong relationship between pork tenderness and consumer like ratings of pork loins. Consequently, there has been interest in development of retail programs based on eating quality and, thus, there has been need for a means to identify pork that excel in tenderness. We have developed a system for on-line classification of beef carcasses for ribeye tenderness using visible and near-infrared spectroscopy. Therefore, the present experiment was conducted to develop a non-invasive, on-line method to predict tenderness of pork loins. Prediction models were developed that successfully classified “predicted tender” and “not predicted tender” groups such that “predicted tender” was much more tender with fewer extremely tough loins. This study resulted in the development and validation of a system for on-line prediction of pork loin tenderness. Use of this technology could allow pork processors to identify pork loins with more consistent tenderness for marketing premium branded product lines.

Technical Abstract:
Boneless pork loins (n = 901) were evaluated either on the loin boning and trimming line of large-scale commercial plants (n = 465) or at the U.S. Meat Animal Research Center abattoir (n = 436). Exposed LM on the ventral side of boneless loins was evaluated with visible and near-infrared spectroscopy (VISNIR; 450 to 1000 nm) using a commercial system that was developed for on-line evaluation of beef tenderness. Boneless loin sections were aged (2°C) until 14 days postmortem and two 2.54-cm thick chops were obtained from the 11th rib region. Fresh (never frozen) chops were cooked (71°C) and LM slice shear force (SSF) was measured on each of the two chops. Those two values were averaged and that value was used for all analyses. Loins were blocked by plant (n = 3), production day (n = 24), and observed SSF (Mean = 13.9 kg; SD = 3.7; CV = 26.8%; Range 6.4 to 32.4 kg) and one-half of the loins were assigned to a calibration data set, which was used to develop regression equations, and one-half of the loins were assigned to a prediction data set, which was used to validate the regression equations. A partial least-squares regression model was developed and loins were classified as predicted tender or not predicted tender if their VISNIR-predicted SSF was < 14.0 kg or = 14.0 kg, respectively. ANOVA was used to determine the effect of VISNIR classification on SSF. The calibration data set and prediction data set had 61.9% and 60.9% of the loins classified as predicted tender, respectively. For both the calibration data set and prediction data set, mean SSF was lower for loins predicted tender as compared to loins not predicted tender (P < 0.001). Relative to loins that were not predicted tender, the percentage of loins with SSF = 20 kg was lower for loins predicted tender in the calibration data set (3.6 vs 8.1%) and prediction data set (1.8 vs 13.6%). These results clearly indicate that the VISNIR technology could be used to non-invasively classify pork loins on-line for tenderness.